{"id":"https://openalex.org/W4406460607","doi":"https://doi.org/10.1109/bigdata62323.2024.10826047","title":"Differentially Private Synthetic Data Generation Using Context-Aware GANs","display_name":"Differentially Private Synthetic Data Generation Using Context-Aware GANs","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406460607","doi":"https://doi.org/10.1109/bigdata62323.2024.10826047"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10826047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10826047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.08869","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062851464","display_name":"Anantaa Kotal","orcid":"https://orcid.org/0000-0003-1818-9705"},"institutions":[{"id":"https://openalex.org/I164936912","display_name":"The University of Texas at El Paso","ror":"https://ror.org/04d5vba33","country_code":"US","type":"education","lineage":["https://openalex.org/I164936912"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anantaa Kotal","raw_affiliation_strings":["The University of Texas at El Paso,Computer Science,El Paso,US"],"affiliations":[{"raw_affiliation_string":"The University of Texas at El Paso,Computer Science,El Paso,US","institution_ids":["https://openalex.org/I164936912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020975010","display_name":"Anupam Joshi","orcid":"https://orcid.org/0000-0002-8641-3193"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anupam Joshi","raw_affiliation_strings":["C.S.E.E. University of Maryland,Baltimore,US"],"affiliations":[{"raw_affiliation_string":"C.S.E.E. University of Maryland,Baltimore,US","institution_ids":["https://openalex.org/I126744593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062851464"],"corresponding_institution_ids":["https://openalex.org/I164936912"],"apc_list":null,"apc_paid":null,"fwci":1.0848,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.82865396,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"6289","last_page":"6297"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11181","display_name":"Advanced Data Storage Technologies","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7006080150604248},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5653936266899109}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7006080150604248},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5653936266899109},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10826047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10826047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2512.08869","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.08869","pdf_url":"https://arxiv.org/pdf/2512.08869","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.08869","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.08869","pdf_url":"https://arxiv.org/pdf/2512.08869","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4406460607.pdf","grobid_xml":"https://content.openalex.org/works/W4406460607.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W1873763122","https://openalex.org/W2134167315","https://openalex.org/W2159024459","https://openalex.org/W2261525379","https://openalex.org/W2473418344","https://openalex.org/W2544063074","https://openalex.org/W2551584695","https://openalex.org/W2789226849","https://openalex.org/W2806276686","https://openalex.org/W2910283416","https://openalex.org/W2952716587","https://openalex.org/W2963052201","https://openalex.org/W2963073614","https://openalex.org/W2963693643","https://openalex.org/W2963800363","https://openalex.org/W2964024144","https://openalex.org/W2967880504","https://openalex.org/W2997634552","https://openalex.org/W3022574011","https://openalex.org/W3071470454","https://openalex.org/W3106873467","https://openalex.org/W3112906095","https://openalex.org/W3123788974","https://openalex.org/W3190683899","https://openalex.org/W4224308799","https://openalex.org/W4224322255","https://openalex.org/W4234726042","https://openalex.org/W4238948521","https://openalex.org/W4288296172","https://openalex.org/W4320013936","https://openalex.org/W4323841407","https://openalex.org/W4388115905","https://openalex.org/W4391096533","https://openalex.org/W4402218656","https://openalex.org/W4403048309","https://openalex.org/W4403795545","https://openalex.org/W4403908451","https://openalex.org/W6735926176","https://openalex.org/W6748503580","https://openalex.org/W6755312952","https://openalex.org/W6757641797","https://openalex.org/W6759397991","https://openalex.org/W6765451912","https://openalex.org/W6787788469","https://openalex.org/W6866512977","https://openalex.org/W6872029013","https://openalex.org/W6873123422"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0,225],"widespread":[1],"use":[2,306],"of":[3,54,105,295],"big":[4],"data":[5,46,68,91,107,174,230,237,276,297],"across":[6,262],"various":[7],"sectors":[8],"has":[9],"brought":[10],"significant":[11],"privacy":[12,43,86,249,321],"concerns,":[13],"particularly":[14],"when":[15],"sensitive":[16,63,253],"information":[17],"is":[18,245],"shared":[19],"or":[20,73,159,187],"analyzed.":[21],"Regulations":[22],"like":[23,114],"GDPR":[24],"and":[25,108,143,221,268,293,315],"HIPAA":[26],"impose":[27],"strict":[28,320],"controls":[29],"on":[30],"handling":[31],"data,":[32,126],"making":[33,302],"it":[34,272,303],"difficult":[35],"to":[36,81,96,184,239],"balance":[37],"the":[38,52,106,124,170,235,243,256,291],"need":[39],"for":[40,76,140,154,178,305],"insights":[41],"with":[42,156,312],"requirements.":[44],"Synthetic":[45,173],"offers":[47],"a":[48,197,213],"promising":[49],"solution,":[50],"enabling":[51],"creation":[53],"artificial":[55],"datasets":[56],"that":[57,101,132,216,271,277,287],"mirror":[58],"real-world":[59],"patterns":[60,122,314],"without":[61,84,176],"exposing":[62],"information.":[64],"For":[65,145],"instance,":[66],"synthetic":[67,90,229,275,296],"can":[69,182],"simulate":[70],"patient":[71,189],"records":[72],"network":[74],"flows":[75],"training":[77,125],"machine":[78],"learning":[79],"models":[80],"conduct":[82],"research":[83],"violating":[85],"laws.":[87],"However,":[88],"traditional":[89],"generation":[92],"methods":[93,118],"often":[94,128],"fail":[95],"capture":[97],"complex,":[98],"implicit":[99,180,222,316],"rules":[100,131,181,211,280],"relate":[102],"different":[103],"elements":[104],"are":[109,133,138],"essential":[110],"in":[111,169,307],"specific":[112,157],"domains":[113],"healthcare.":[115],"While":[116],"these":[117,179,193,232],"might":[119],"replicate":[120],"explicit":[121,220,313],"from":[123,255],"they":[127],"overlook":[129],"domain-specific":[130,210,279],"not":[134,165],"directly":[135],"stated":[136],"but":[137],"critical":[139],"maintaining":[141],"realism":[142,292],"utility.":[144],"example,":[146],"prescription":[147],"guidelines,":[148],"such":[149],"as":[150],"avoiding":[151],"certain":[152],"medications":[153],"patients":[155],"conditions":[158],"preventing":[160],"harmful":[161],"drug":[162],"interactions,":[163],"may":[164],"be":[166],"explicitly":[167,217],"represented":[168],"original":[171,257],"data.":[172,258],"generated":[175,236],"accounting":[177],"lead":[183],"medically":[185],"inappropriate":[186],"unrealistic":[188],"profiles.":[190],"To":[191],"address":[192],"limitations,":[194],"we":[195],"propose":[196],"framework":[198,208],"called":[199],"Context-Aware":[200],"Differentially":[201],"Private":[202],"Generative":[203],"Adversarial":[204],"Network":[205],"(ContextGAN).":[206],"Our":[207,284],"integrates":[209],"using":[212],"constraint":[214],"matrix":[215],"encodes":[218],"both":[219,310],"domain":[223,240,300],"knowledge.":[224],"constraint-aware":[226],"discriminator":[227,244],"evaluates":[228],"against":[231],"rules,":[233,317],"ensuring":[234,248],"adheres":[238],"constraints.":[241],"Furthermore,":[242],"differentially":[246],"private,":[247],"preservation":[250],"by":[251,298],"protecting":[252],"details":[254],"We":[259],"validate":[260],"ContextGAN":[261,288],"multiple":[263],"domains,":[264],"including":[265],"healthcare,":[266],"security,":[267],"finance,":[269],"demonstrating":[270],"produces":[273],"high-quality":[274],"respects":[278],"while":[281],"preserving":[282],"privacy.":[283],"results":[285],"show":[286],"significantly":[289],"improves":[290],"utility":[294],"enforcing":[299],"constraints,":[301],"suitable":[304],"scenarios":[308],"requiring":[309],"compliance":[311],"all":[318],"under":[319],"guarantees.":[322]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}